I-DEEC: improved DEEC for blanket coverage in heterogeneous wireless sensor networks

  • Vibha NehraEmail author
  • Ajay K. Sharma
  • Rajiv K. Tripathi
Original Research


Event critical applications demand blanket coverage. On the other hand, nodes closer to the base station are exploited as they have to spend additional energy in relaying data of far away nodes. This brings in the idea of implementing blanket coverage in heterogeneous wireless sensor networks. I-DEEC improvises distributed energy efficient clustering (DEEC) by deploying network nodes in two layers. Layer 1 strategically tessellate hexagons to deploy nodes as normal or super nodes based on distance from the base station, considering the high data requirement within hop distance around the base station. Layer 2 randomly deploys advanced nodes with condition that no two advanced nodes sense the same area. Further, it uses the sum of the ratio of node’s distance to the base station along with residual energy ratio to calculate the possibility of a node to be selected as a cluster head, followed by the selection of the optimal percentage high possibility nodes as cluster heads. I-DEEC provisions blanket coverage by extending the stability period by reducing the ratio between initial energy of different types of nodes. I-DEEC revamps DEEC protocol in terms of network lifetime, percentage area coverage, throughput, and residual energy.


Blanket coverage Heterogeneous network Stability period Initial energy Hexagon covering 



  1. Abdollahzadeh S, Navimipour NJ (2016) Deployment strategies in the wireless sensor network: a comprehensive review. Comput Commun 91–92:1–16. CrossRefGoogle Scholar
  2. Andersen T, Tirthapura S (2009) Wireless sensor deployment for 3d coverage with constraints. In: Proceedings of sixth international conference on networked sensing systems at Pittsburg Carnegie Mellon UniversityGoogle Scholar
  3. Anuradha D, Srivatsa SK (2019) Energy effectual reconfigurable routing protocol (E2R2P) for cluster based underwater wireless sensor networks. J Ambient Intell Human Comput. CrossRefGoogle Scholar
  4. Bhola J, Soni S, Cheema GK (2019) Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. J Ambient Intell Human Comput. CrossRefGoogle Scholar
  5. Chakrabarty K, Iyengar SS, Qi H, Cho E (2002) Grid coverage for surveillance and target location in distributed sensor network. IEEE Trans Comput 51:1448–53MathSciNetCrossRefGoogle Scholar
  6. Chieh K, Liu BH, Tsai MJ (2011) The critical-square grid coverage problem in wireless sensor network is np-complete. Comput Netw 55:2209–2220. CrossRefGoogle Scholar
  7. Gupta P, Sharma AK (2019) Designing of energy efficient stable clustering protocols based on BFOA for WSNS. J Ambient Intell Human Comput 10(2):681–700CrossRefGoogle Scholar
  8. Heinzelman W, Chandrakasan AP, Balakrishnan H (2002) An application specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRefGoogle Scholar
  9. Kumar D (2014) Performance analysis of energy efficient clustering protocols for maximizing lifetime of wireless sensor networks. IET Wirel Sens Syst.
  10. Li J, Mohapatra P (2005) An analytical model for the energy hole problem in many to one sensor networks. IEEE (0-7803-9152-7)Google Scholar
  11. Liu M, Cao J, Chen G, Wang X (2009) An energy aware routing protocol in wireless sensor networks. Sensors 9:445–462. CrossRefGoogle Scholar
  12. Melissen JBM, Schuur PC (2000) Covering a rectangle with six and seven circles. Dicrete Appl Math 99:149–156MathSciNetCrossRefGoogle Scholar
  13. Nurmela KJ, Ostergard PRJ (2000) Covering a square with up to 30 equal circles. HUT-TCS-A62 EspooGoogle Scholar
  14. Priyadarshini RR, Sivakumar N (2019) Enhancing coverage and connectivity using energy prediction method in underwater acoustic wsn. J Ambient Intell Human Comput 1–10Google Scholar
  15. Qing L, Zhu Q, Wang M (2006) Design of distributed energy efficient clustering algorithm for heterogeneous wireless sensor networks. Comput Commun 29:2230–2237CrossRefGoogle Scholar
  16. Rebai M, Berre ML, Snoussi H, Khoukhi L (2015) Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks. Comput Oper Res. MathSciNetCrossRefGoogle Scholar
  17. Sharma V, Patel RB, Bhadauria HS, Prasad D (2016) Deployment schemes in wireless sensor network to achieve blanket coverage in large scale open area: a review. Egypt Inform J 17:45–56. CrossRefGoogle Scholar
  18. Singh S (2017) Energy efficient multilevel network model for heterogeneous wsns. Eng Sci Technol Int J 20(1):105–115CrossRefGoogle Scholar
  19. Singh S (2019) A proficient node deployment mechanism using adjustable sensing range in wireless sensor networks. Iran J Sci Technol Trans Electr Eng 43(1):191–199CrossRefGoogle Scholar
  20. Singh S, Malik A (2017a) hetdeec: heterogeneous deec protocol for prolonging lifetime in wireless sensor networks. J Inf Optim Sci 38(5):699–720MathSciNetGoogle Scholar
  21. Singh S, Malik A (2017b) hetsep: heterogeneous SEP protocol for increasing lifetime in wsns. J Inf Optim Sci 38(5):721–743MathSciNetGoogle Scholar
  22. Singh S, Malik A, Kumar R (2017) Energy efficient heterogeneous DEEC protocol for enhancing lifetime in wsns. Eng Sci Technol Int J 20(1):345–353CrossRefGoogle Scholar
  23. Smaragdakis G, Matta I, Bestavros A (2004) SEP: a stable election protocol for clustered heterogeneous wireless sensor networks. Boston University Computer Science Department, Tech. repGoogle Scholar
  24. Srivastava V, Tripathi S, Singh K et al (2019) Energy efficient optimized rate based congestion control routing in wireless sensor network. J Ambient Intell Human Comput. CrossRefGoogle Scholar
  25. Tarnai T, Gaspar Z (1995) Covering a square by equal circles. ElMath 50Google Scholar
  26. Wang CY, Hu CC, Tseng YC (2008) Efficient placement and dispatch of sensors in a wireless sensor network. IEEE Trans Mob Comput. CrossRefGoogle Scholar
  27. Wu X, Chen G, Das SK (2008) Avoiding energy holes in wireless sensor networks with nonuniform node distribution. IEEE Trans Parallel Distrib Syst. CrossRefGoogle Scholar
  28. Zhao X, Xiong X, Sun Z, Zhang X, Sun Z (2019) An immune clone selection based power control strategy for alleviating energy hole problems in wireless sensor networks. J Ambient Intell Human Comput. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology DelhiDelhiIndia
  2. 2.Department of Computer Science and EngineeringNational Institute of Technology JalandharJalandharIndia
  3. 3.Department of Electronics and Communication EngineeringNational Institute of Technology DelhiDelhiIndia

Personalised recommendations